UNICEF Child Deprivation Report

This report shows how many children in different countries are suffering. We can see that in some countries, things are getting better, but in others, they are still very bad

Code 
from google.colab import drive
drive.mount("/content/drive")
Mounted at /content/drive
Code 
!pip install quarto-cli
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Code 
import pandas as pd
import plotnine as p9
from plotnine import *
import numpy as np
Code 
indicator_1 = pd.read_csv("unicef_indicator_1.csv")
metadata = pd.read_csv("unicef_metadata.csv")
Code 
indicator_1 = indicator_1[indicator_1['sex'] == 'Total']
indicator_1 = indicator_1.rename(columns={'country': 'Country', 'time_period': 'Year', 'obs_value': 'Value'})
indicator_1 = indicator_1[['Country', 'Year', 'Value']]
Code 
top_n = 10
top_countries = indicator_1.nlargest(top_n, 'Value')

bar_chart = (
    ggplot(top_countries, aes(x='Country', y='Value'))
    + geom_bar(stat='identity', fill='steelblue')
    + coord_flip()
    + theme_minimal()
    + labs(title=f'Top {top_n} Countries by Child Deprivation Index')
)

bar_chart # This will display the chart

###šŸ“Š The bar chart illustrates the top 10 countries by Child Deprivation Index. Madagascar has the highest deprivation, followed by Ethiopia, Chad, and Cambodia. Other countries on the list include Niger, Mozambique, Mauritania, Liberia, Benin, and Angola. Most of these countries are in Africa, highlighting significant regional disparities in child wellbeing.

Code 
import pandas as pd
from plotnine import ggplot, aes, geom_line, geom_point, theme_minimal, labs

# Load your datasets
indicator_1 = pd.read_csv("unicef_indicator_1.csv")
metadata = pd.read_csv("unicef_metadata.csv")

# Clean indicator_1 data
indicator_1 = indicator_1[indicator_1['sex'] == 'Total']
indicator_1 = indicator_1.rename(columns={'country': 'Country', 'time_period': 'Year', 'obs_value': 'Value'})
indicator_1 = indicator_1[['Country', 'Year', 'Value']]

# Get top countries from indicator data based on average 'Value'
top_countries = indicator_1.groupby('Country')['Value'].mean().nlargest(10).index.tolist()

# Filter the indicator data for only the top countries
multi_country_data = indicator_1[indicator_1['Country'].isin(top_countries)]

# Create a time series chart for the top countries
multi_time_series_chart = (
    ggplot(multi_country_data, aes(x='Year', y='Value', color='Country'))
    + geom_line()
    + geom_point()
    + theme_minimal()
    + labs(
        title='Child Deprivation Index Over Time: Top 10 Countries',
        x='Year',
        y='Deprivation Index'
    )
)

multi_time_series_chart  # Display the chart
/usr/local/lib/python3.11/dist-packages/plotnine/geoms/geom_path.py:113: PlotnineWarning: geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?

##šŸ“‰ Child Deprivation Index Over Time: Top 10 Countries

This chart illustrates the Child Deprivation Index over time for the top 10 countries. The x-axis represents the year, ranging from 2012 to 2018, while the y-axis represents the Deprivation Index. Each line represents a different country: Angola, Benin, Cambodia, Chad, Ethiopia, Liberia, Madagascar, Mauritania, Mozambique, and Niger. The chart shows how the Deprivation Index has changed for each of these countries over the specified period.

Code 
!pip install plotly
import plotly.express as px
Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (5.24.1)
Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly) (9.1.2)
Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from plotly) (24.2)
Code 
fig = px.choropleth(indicator_1,
                    locations="Country",
                    locationmode='country names',
                    color="Value",
                    title="Child Deprivation Index by Country")
fig.show()

šŸŒ The Child Deprivation Index by Country is displayed, with a color gradient indicating values from 0 to 12. Countries in darker shades of blue have lower deprivation index values, while those in orange and yellow have higher values. Much of North America, Europe, and Australia appear to have low levels of child deprivation. Parts of Africa and South Asia, including India, show relatively high deprivation levels based on the index. The index provides a snapshot of the disparities in child well-being across the globe.

Code 
scatter_plot = (
    ggplot(indicator_1, aes(x='Year', y='Value'))
    + geom_point()
    + geom_smooth(method='lm', color='red')
    + labs(title='Child Deprivation Over Time', x='Year', y='Deprivation Index')
    + theme_minimal()
)

print(scatter_plot)  # This will display the chart
<ggplot: (640 x 480)>
Code 
scatter_plot

šŸ“ˆ The scatter plot titled ā€œChild Deprivation Over Timeā€ illustrates the trend of the Deprivation Index from 2011 to 2018. Individual data points are scattered across the years, with a linear regression line indicating a slight downward trend in the deprivation index over time. The grey area around the regression line represents the confidence interval. While thereā€™s variability in deprivation levels each year, the overall trend suggests a marginal decrease in child deprivation during the observed period.